Abstract : This paper focuses on solving the problems of preparing and normalizing data that are captured from a classroom observation, and are linked with significant relevant properties. We adapt these data using a Bayesian model that creates normalization conditions to a well fitted artificial neural network. We separate the method in two stages: first implementing the data variable in a functional multi-factorial normalization analysis using a normalizing constant and then using constructed vectors containing normalization values in the learning and testing stages of the selected learning vector quantifier neural network.
Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.30-36, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_7〉
https://hal.inria.fr/hal-01060648
Contributeur : Hal Ifip
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Soumis le : vendredi 17 novembre 2017 - 14:21:57
Dernière modification le : lundi 18 décembre 2017 - 01:11:00
Document(s) archivé(s) le : dimanche 18 février 2018 - 15:18:03
Marios Poulos, Vassilios S. Belesiotis, Nikolaos Alexandris. A Classroom Observation Model Fitted to Stochastic and Probabilistic Decision Systems. Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.30-36, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_7〉. 〈hal-01060648〉
